Videogame “Addiction” Versus “Problematic Play”

Editorial

iMedPub Journals Acta Psychopathologica 2015 http://www.imedpub.com ISSN 2469-6676 Vol. 1 No. 3:16

DOI: 10.4172/2469-6676.100016

Videogame “Addiction” Versus Gregory S. Pouliot1, 2, Ethan M. Paschall2 and “Problematic Play”: Karen K. Saules2

Which Construct Best Captures 1 McLaren Flint Hospital, Flint, MI, United States the Nature of Excessive 2 Eastern Michigan University, Ypsilanti, Videogame use? MI, United States

Corresponding author: Ethan Paschall

Abstract  [email protected] As the number of individuals who play videogames has increased in recent years, the frequency with which patients seek treatment for problematic videogame Department of psychology, Eastern playing (PVGP) behaviors has also risen. Thus, explorations into the specific Michigan University, Ypsilanti, MI, 48197, characteristics of PVGP are essential now more than ever before. However, the United States. current state of the literature primarily relies on comparisons between PVGP and pathological gambling, utilizing modified measures of the latter to assess the Tel: (739) 487-1155 former. More recently, scales have emerged to tap the DSM-5 criteria for Internet Gaming Disorder. However, to date, no studies have attempted to adapt the Citation: Pouliot GS, Paschall EM, Saules KK. diagnostic criteria for substance use disorder (SUD), specifically, in an effort to Videogame “Addiction” Versus “Problematic understand PVGP within the context of addiction, and currently no single measure Play”: Which Construct Best Captures the exists that adequately captures the full DSM-5 criteria for SUD or “addiction.” Nature of Excessive Videogame use? Acta The current study sought to address these questions by adapting SUD criteria Psychopathol. 2015, 1:3. to address videogame-related behavior via a measure we call the Videogame Addiction Scale (VGAS). Comparisons of the psychometrics and criterion validity of the VGAS with existing measures of PVGP suggested the VGAS was superior. Lastly, a model of videogame addiction was generated that aligns with the SUD literature. Specifically, impulsivity, maladaptive coping, weekly game playing time (akin to “dose”), and particular structural game characteristics (akin to “route of administration” in addiction) were associated with problematic videogame play when conceptualized as an addiction. Results suggest that the addiction construct, in contrast to “problematic play,” best captures the underlying features of excessive videogame play. Keywords: Problematic videogame play; Addiction; Substance use disorder

Received: August 12, 2015; Accepted: October 02, 2015; Published: October 12, 2015

Introduction reported that the average household in the United States owns at least one device capable of playing a videogame, the average Developing a specific definition for a “videogame” is an ever- gamer is 30 years old, and the total revenue for the videogame evolving process, as technology continually changes this term’s industry as a whole in 2011 was $24.75 billion. Clearly, playing referent. While the central tenet of playing an electronic game some form of videogame has become a normative experience for through a visual display remains static, the games themselves individuals across the entire demographic spectrum. However, have grown in significant ways and changed the landscape of this has also opened up entirely new sects of the population to plot-based media from a passive (e.g., television) into an active the possibility of problematic play that may result in a host of process. The Entertainment Software Association (2012) [1] has psychological, behavioral, and interpersonal problems.

The literature is still unclear about the level of “pathology” analogous to the symptoms of substance use disorders” [12]. associated with videogame play, and the term “addiction” While only one study to date has evaluated the diagnostic references specific features that have not been conclusively accuracy of IGD specifically [13], other studies have highlighted demonstrated with regard to videogame play [2]. Thus, the addictive quality of videogames [14, 15]. One issue with “problematic videogame play” (PVGP) has been suggested as the current conceptualization of videogame addiction in DSM- the most appropriate term [3, 4]. Although the conceptualization 5 is that it emphasizes the internet as a functional component of PVGP and its accompanying symptomatology varies across of the playing process. As King and Delfabbro [16] highlight, this studies, most researchers agree that the term refers to negative “confuses two different delivery mechanisms (i.e., the internet consequences resulting from game play, not simply the amount and a video-game) within a single classification” (p. 21). Similarly, of time spent playing. This constellation of symptoms that Baer, Saran & Green [17] report high correlations between encapsulates more than just excessive play may constitute measures assessing online and offline videogame use and further a behavioral addiction, with symptoms that align with those highlight that videogame playing (regardless of online status) “is characteristic of addictive disorders. For example, impulsivity consistently correlated with emotional and functional problems is a hallmark of substance use disorders, and this same trait is in youth across multiple measures of addiction and impairment observed amongst those with PVGP, suggesting a potential as well as multiple informants” (p. 430). Thus, the DSM-5 for common etiological factors [5, 6]. A parallel can be drawn conceptualization may be focusing too narrowly on one subtype from PVGP and SUD when impulsivity is examined at both the of videogames (i.e., those played online). Further, a recent behavioral and electrophysiological levels, lending further review of the neuroscience literature highlights that “Internet gaming addiction appears similar to other addictions, including support to shared features across PVGP and SUD [7]. Therefore, substance-related addictions, at the molecular, neurocircuitry, the potential for using the DSM-5 criteria for substance-related and behavioral levels” [18]. Thus, increasing evidence for an disorders as a model from which to adapt our conceptualization addiction model of videogame playing has been materializing. of PVGP will be explored. Towards this end, we used items from However, one of the main issues that prevented IGD from validated measures of PVGP [8] to tap specific DSM-5 symptoms, being formalized within the DSM-5 was the lack of standardized as no single measure of PVGP currently exists that adequately diagnostic criteria across the current literature [19]. Additionally, captures the full DSM-5 criteria for SUD or “addiction” [9]. there are concerns that inclusion of the disorder will eventually In order to conceptualize PVGP as an addictive behavior, it lead the term “addiction” to be used for any excessive behavior must first be assessed in accordance with establish criteria for that causes problems [20]. addictive disorders to allow for the comparison with traditional addictions, such as substance use disorder. This marks the first This lack of a standardized measure for assessing the criteria and attempt to move the field of video game research in the direction severity of IGD has recently led to the creation of a number of of measuring PVGP as an addiction, without simply defining the scales that attempt to capture this problematic behavior. Unlike behavior as atheoretically “pathological”. the present study, however, these have adhered to the DSM-5 suggested symptomology for IGD, rather than mirroring SUD Blaszczynski [10] has noted that simply experiencing negative symptomology, specifically. consequences from excessive playing, a common focus of current Rehbein, Kliem, Baier, Moble and Petry (2015) [21] created the literature is not sufficient for addiction. He argues that impaired Video Game Dependency Scale by adapting an existing measure control, which is central to the concept of addiction, has not been to cover the nine DSM-5 criteria required for IGD and tested effectively demonstrated. Referencing internet addiction, Shaffer, its validity on a sample of German adolescents, resulting in Hall, and Vander Bilt [11] argue that so-called technological strong reliability of this measure. This scale represents a highly addictions may represent manifestations of other disorders or conservative method of assessing IGD and was constructed in a maladaptive patterns and are not necessarily unique psychiatric similar way to Cho et al.’s [22] Internet Addiction (IA) scale, which conditions. Therefore, there is presently considerable controversy gathered items from previous internet addiction scales in order in the literature as to whether excessive gaming constitutes to create a new scale aimed at assessing generalized internet addictive behavior or simply some variant of “problematic” addiction. Pontes, Király, Demetrovics and Griffiths [23] assessed behavior. Regardless, it is generally agreed upon that excessive both online and offline gaming addiction and created the Internet game playing can have personal and social consequences, and, as and Gaming Disorder Test (IGD-20), which has high correlations such, a number of measures have been developed to assess this between the conceptualized 6 factors measured and IGD’s 9 phenomenon. criteria for diagnosis. This new measure was further refined The DSM-5 [12] includes Internet Gaming Disorder (IGD) under the and shortened to a 9 item version that serves as a brief yet valid “Conditions for Further Study” section and further exemplifies tool for assessing IGD [24]. Lemmens, Valkenburg, Gentile, [25] that gaming may represent an addiction. Specifically, the DSM-5 also tested both dichotomous and polytomous scales as well as highlights that videogame playing shares similarities with SUDs varying measure length in the creation of four different survey and even notes that the Chinese government has labeled internet measures based on the conception of IGD as comprised of a single gaming as an addiction. IGD is characterized as “a pattern of factor made up of 9 items representative of the DSM-5 criteria. excessive and prolonged Internet gaming that results in a cluster Regardless of what psychometric tools ultimately become the of cognitive and behavioral symptoms, including progressive loss standard practice within the research, diagnosis, and treatment of control over gaming, tolerance, and withdrawal symptoms, of IGD, it is imperative that IGD is examined within the context 2 This article is available from: www.psychopathology.imedpub.com Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

of traditional methods of assessing behavioral addiction. It is Participants were asked to rate how much they enjoyed each important to not only pursue a valid and reliable measure, but feature, the overall importance of each feature, and how much also to examine how the constructs of IGD and PVGP are related that feature related to time playing games. Problematic players to other factors associated with methods of understanding had higher enjoyment of adult content, finding rare items, addiction that have been studied extensively, such as the criteria watching cut-scenes, and the tactile sensation of the controller. and methods of assessing SUD within individuals. They further reported managing in-game resources, earning points, getting 100% in the game, and mastering the game as In an attempt to identify the psycho-structural characteristics the most important aspects. Lastly, leveling up, earning meta- unique to videogame playing, Wood, Gupta, and Derevensky [26] game rewards, and fast loading times had the greatest behavioral assessed which game features gamers found most important. impact with regard to length of play. The primary categories assessed consisted of sound (sound Although the Video Game Structural Characteristics Survey effects, speaking characters, background music, narration), requires psychometric validation and replication is sorely needed, graphics (realistic graphics, cartoon graphics, full motion this study represents one of the first attempts to determine which video), background and setting (based on an existing story, specific aspects of videogames may differentiate problematic realistic or fantasy settings), duration of game (long, medium, players from normative videogamers. This is especially important or short), rate of play (absorption rate), advancement rate (how with modern videogames that no longer mirror real-world fast the game play advances), use of humor, control options “games” with opponents trying to beat each other and instead (player adjustable difficulty, controls, etc.), game dynamics represent alternate realities in which players reside. Critics of (exploration, quest fulfillment, collecting items, puzzle solving, the concept of videogame addiction have specifically argued that etc.), winning and losing features (especially points), character these digital playgrounds are too complicated to manifest a simple development (customization options), brand assurance (brand behavioral addiction analogous to gambling. Thus, attempts at loyalty), and multiplayer features (online vs. local, cooperative understanding which features relate to problematic play can help vs. competitive). Results demonstrated that participants quell these concerns and aid in diagnostic understanding. placed the highest importance on realistic sound, graphics, Given the debate as to whether excessive game play is a symptom and setting. This suggests that for many gamers, realistic, high of some other unresolved problem or an addictive disorder in its quality experiences are more attractive and potentially more own right, the primary aim of the present study was to determine addicting. Interestingly, when females were analyzed separately if a measure that maps onto Substance Use Disorder (SUD) criteria (due to their underrepresentation in the general sample), results would be conceptually valid and relate in meaningful fashion to a indicated that women were more likely to prefer nonviolent, less set of variables that are well-documented to be associated with competitive, gentler-paced, cartoon-style games that involve addictive disorders in general, i.e., impulsivity [30], depression fantasy instead of realism. Thus, gender differences may also exist [31], self-consciousness [32], poor coping skills [33], and family regarding what is most reinforcing about the gaming experience. history of addiction [34]. Due to the abundance of research King, Delfabbro and Griffiths [27] have since expanded upon that went into identifying criteria used for addictive behaviors, Wood et al.’s [26] taxonomy by addressing some of its limitations, it was hypothesized that a DSM-5 conceptualization of PVGP such as refining variables that were difficult to operationalize would have better internal consistency and a more robust factor and better incorporating gambling structural characteristics [28]. analytic structure than traditional measures of PVGP that are Further, King and colleagues [27] noted that their classifications not empirically or theory-driven. It was also predicted that the were designed to help future researchers identify the factors modified DSM-5 conceptualization of PVGP would have greater that contribute to problematic videogame playing. This model criterion validity than existing measures of PVGP. Specifically, an consists of the following five features and their respective sub- aggregated SUD-based score, using items reflective of each DSM- features: Social Features (social utility features, social formation/ 5 SUD symptoms, was expected to yield stronger relationships institutional features, leader board features, support network with constructs generally associated with addiction than would features), Manipulation and Control Features (user input scores on existing measures of PVGP, discussed below. Finally, features, save features, player management features, non- we present a structural model that captures the risk factors controllable features), Narrative and Identity Features (avatar for gaming addiction in accordance with factors known to be creation features, storytelling device features, theme and genre associated with addictive behavior. features), Reward and Punishment Features (general reward type features, punishment features, meta-game reward features, Method intermittent reward features, negative reward features, near miss features, event frequency features, event duration features, Participants payout interval features), and Presentation Features (graphics Participants (N=1,103) were recruited from an undergraduate and sound features, franchise features, explicit content features, student population at a large Midwestern University through in- in-game advertising features). class announcements and a cloud-based participant management In a follow-up paper, King, Delfabbro and Griffiths [29] assessed software system (SONA-systems.com). Inclusionary criteria videogame players recruited via online advertisements using consisted of being at least 18 years old and playing videogames their Video Game Structural Characteristics Survey, a 37-item at least 1 hour a week. The sample reflected an equal percentage self-report measure based on the aforementioned taxonomy. of both male (n=459) and female (n=534) participants whose

ages ranged from 18 to 66, with the overall sample matching the strong desire or urge to use the substance. In an effort to capture typical college-age population (M = 21.36, Mdn= 20, SD = 4.44). the future-oriented aspect of cravings (e.g., the preoccupation The predominant ethnicities within our sample were Caucasian with wanting to use the substance again), item 11 on the PVGP-R (57.9%) and African American (22.0%). The average time spent (“When I am not playing video games, I am often planning how playing videogames on a week day was 4.34 hours and the I will play my next game”) was chosen. Criterion 5 refers to a average time spent playing during a weekend day was 5.37 hours failure to fulfill major role obligations at work, school, or home (Table 1). due to substance use. Interestingly, several measures of PVGP merge school/work problems with interpersonal problems, Procedure which represent separate criteria for SUD. Thus, item 25 of the Before data collection began, study procedures were approved by PVGP-R (“In order to play video games, I have skipped class or the Eastern Michigan University Institutional Review Board. Study work”) was chosen, as it does not include other functional volunteers first read and digitally signed an informed consent impairments. Criterion 6 represents persistent or recurrent social agreement before being eligible to continue. All participants or interpersonal problems and was captured via item 20 on the were placed into a raffle with the possibility of winning one of PVGP-R (“In order to play video games, I get into arguments with two $50 Amazon.com gift cards. people”). Criterion 7 captures the reduction or giving up of social, occupational, or recreational activities and is reflected in item 2 of Measures the PVGP-R (“Because of my video game playing, I have spent less time with my friends and family”). Criterion 8, which highlights Demographic Information: Demographic information was physically hazardous situations related to substance use, was collected, including age, gender, ethnicity, socioeconomic status, not assessed, as videogames do not have an obvious parallel. years of education, current marital status, current employment Criterion 9 assesses continued substance use despite physical status, economic status of current household, and annual or psychological problems. With the exception of the PVGP-R, household income. no other measure explicitly captures the physical problems Problematic Video Game Play - Revised (PVGP-R) Scale: The associated with problematic videogame playing. However, the PVGP-R is the newest version of the scale created by Tolchinsky PVGP-R includes items referencing neck pain (#7), wrist pain and Jefferson [3]. Questions address the psychological, physical, (#14), headaches (#22), hand pain (#24), and back pain (#33). and social consequences of problematic gameplay. Internal Inclusion of physical effects from problematic playing allows for an consistencies for each subscale ranged from α = .76 to α = .85, extension beyond solely psychological effects and creates a closer and construct validity was demonstrated using average number analogue to SUD. Thus, affirmation of any of the aforementioned of hours spent playing [35]. items on the PVGP-R was considered as endorsement of criterion Video-Game Use (VGU): The VGU is an 11-item measure of 9. Criterion 10 captures the concept of tolerance, defined as PVGP created by Gentile [36]. Rated on a 3-point scale (“yes,” either a need for increased amounts to achieve the desired effect “no,” or “sometimes”), the VGU is adapted from the criteria or as a diminished effect when using the same amount. Item 2 for pathological gambling, although the wording of some items of the VGU (“Do you need to spend more and more time and/ closely resembles SUD constructs. Internal consistency was or money on video games in order to feel the same amount reported at α = .78. of excitement?”) alone captures the SUD conceptualization of tolerance and thus was included in this aggregation. Lastly, Videogame Addiction Scale (VGAS): As highlighted earlier, no Criterion 11 refers to withdrawal, which includes either the single measure of PVGP effectively captures the DSM-5 criteria characteristic withdrawal symptoms of a substance or the use of for SUD [9, 37]. Thus, items were chosen from existing measures the substance to avoid these withdrawal symptoms. Given that to develop a set of items that mapped onto SUD criteria. These pathological gambling literature has consistently shown that items were not re-administered; rather, answers to them were withdrawal from gambling typically includes restlessness and lifted from responses to the above-described measures, to create a VGAS score used in the analyses described below. SUD Criterion irritability [38], similar symptom markers were used to assess 1, referencing usage in larger amounts or over a longer period videogame withdrawal. Item 4 of the VGU (“Do you become than was intended, was assessed by item 23 on the PVGP-R (“I restless or irritable when attempting to cut down or stop playing play video games over a longer time period than I intended”). video games?”) appeared to suitably capture the concept. The second criterion, which highlights a persistent desire or Withdrawal symptoms have been reported in individuals with unsuccessful effort to cut down or control substance use, was gambling disorder and some substance use disorders as well as in captured in the third item of the VGU (“Have you tried to play individuals who engage in PVGP, with reports of similar symptom video games less often or for shorter periods of time, but were manifestation when there is an inability to initiate or purposefully unsuccessful?”). Criterion 3 requires that a great deal of time stop engaging in the activity [8]. is spent in obtaining, using, or recovering from substance use. Merged together, these 10 items represented the 10 SUD criteria Given that obtainment of and recovery from videogames are measured in this study. However, the PVGP-R and VGU utilize not nearly as problematic as for psychoactive substances, item different metrics for responding, with the former employing a 6 of the PVGP-R (“I spend an increasing amount of time playing 5-point Likert scale and the latter a 3-point scale. To correct for this, video games”) was deemed to assess the problematic amount of scores on VGU items were mapped onto a 5-point scale such that time invested. DSM-5 Criterion 4 assesses cravings, defined as a 4 This article is available from: www.psychopathology.imedpub.com Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

Table 1 Participant Characteristics. Number of Participants Percentage of Sample Gender Male 459 45.3% Female 534 52.7%

Ethnicity Caucasian 587 57.9% African American 223 22.0% Middle Eastern 32 3.2% Asian 28 2.8% Other 39 3.9% Multiracial 94 9.3%

Relationship Status Single 529 52.2% Non-cohabiting 350 34.6% Romantic Relationship Married 64 6.3% Living with a Partner 49 4.8% Other 10 1.0%

Average Time Spent Playing Videogames On a Week Day 4.34 Hours SD = 2.99 On a Weekend Day 5.37 Hours SD = 3.16

“no” becomes “never” (1), “maybe” becomes “sometimes” (3), AUDIT: The Alcohol Use Disorders Identification Test (AUDIT; and “yes” becomes “often” (5). Although another option would [39] is generally considered one of the best screening tools have been to mathematically transform the 3 anchors to fit on a for assessing a range of alcohol problems (Fiellin, Carrington, 5-point scale (e.g., multiply each score by 1.667), this would place O’Connor, 2000) [40]. a negative response in between “never” and “sometimes” on the Brief Young Adult Alcohol Consequences Questionnaire PVGP-R scale. Thus, individuals who have never experienced the (B-YAACQ): The B-YAACQ [41] is a 24-item measure of problematic symptom of a given item would have artificially inflated scores on drinking specifically for use with college students. This measure the three items from the VGU as compared to the seven PVGP-R has been shown to have high internal consistency, minimal item items. It should be noted that the complete PVGP-R and VGU redundancy, reliability over time, sensitivity to change, and does measures were administered as part of the online questionnaire, not yield floor or ceiling effects [42]. negating the need to form a specific survey or to group items accordingly. This allowed for the evaluation of PVGP as intended Family History of Addiction: One item from the South Oaks by the scales’ creators in addition to the examination of SUD- Gambling Screener (SOGS) [43] assessing “Which of the following people in your life has (or had) a gambling problem” was adapted adapted criteria. for the current study. Specifically, participants completed Brief Version of Video Game Structural Characteristics: The this question with regard to SUDs, gambling, and videogame original version of this measure [29] included 111 items assessing playing. In the current study, three variables were constructed game features related to social aspects, manipulation and and reported: total individuals with a gambling problem, total control, narrative and identity, reward and punishment, as well number with an alcohol or other drug (AOD) problem, and total as presentation. Specifically, 37 structural characteristics were number with a videogame problem. rated on three items assessing enjoyment (“How much do you Patient Health Questionnaire – Depression Scale (PHQ-9): The enjoy this feature of the video game?”), perceived importance PHQ-9 [44] is the 9-item depression subscale of the PHQ, which (“How important do you believe this feature is to the playing assesses the level of depression over the past two weeks using experience?”), and behavioral impact (“What is the extent a 4-point Likert scale ranging from “Not at all” to “Nearly every to which this feature contributes to longer playing times?”). day.” Total scores range from 0 to 27, with suggested severity Responses were scored on a 5-point Likert scale ranging from categories of 0-4 (none), 5-9 (mild), 10-14 (moderate), 15 -19 “Not at all” to “High importance.” However, King and colleagues (moderately severe), and 20-27 (severe). [29] highlighted only the top 15 characteristics for each question, as the authors noted that these had the strongest statistical Barratt Impulsiveness Scale, Version 11 (BIS-11): The BIS-11 findings and the most utility. Thus, in an effort to increase the [45] is a commonly utilized measure of impulsivity. Consisting of brevity of this measure, only the top 15 items on enjoyment, 30 items rated on a 4-point Likert scale (ranging from “Rarely/ perceived importance, and behavioral impact were used for the Never” to “Almost always/Always”), the BIS-11 comprises three current study. subscales: motor impulsivity (action without thought), non-

planning impulsivity (lack of future orientation), and cognitive arrangement of variables. In order to address any missing data, impulsivity (poor attention and concentration). the CFA used the full information maximum likelihood (FIML) method, which allowed Mplus to estimate the missing values. Self-Consciousness Scale (SCS): The SCS [46] measures self- consciousness in three domains: private self-consciousness Results (attending to inner thoughts and feelings), public self- consciousness (general awareness of the self in a social context), VGU: As this measure is conceptualized as being unidimensional, and social anxiety (the experience of discomfort in the presence an initial CFA was conducted using the validation sample to of others). The measure consists of 23 items rated on a 5-point determine if a one-factor solution adequately fit the data; results Likert scale, ranging from “Extremely Uncharacteristic” to suggested poor fit using the CFI (.72) and RMSEA (.12) fit indices, “Extremely Characteristic.” χ2 (44) = 345.71, p < .001. This suggests that the 11 items of the VGU likely represent more than one latent construct and would Brief Coping Orientation for Problems Experienced (COPE) likely load onto several factors. Inventory: The Brief COPE [47] is a 28-item measure of the way in which individuals cope with stress. Its 14 subscales include Thus, an EFA was conducted on the VGU items for participants in active coping, planning, positive reframing, acceptance, humor, the validation sample. Regarding the Kaiser-Guttman rule [49], religion, using emotional support, using instrumental support, the eigenvalues suggested a 3-factor solution. For the screen self-distraction, denial, venting, substance use, behavioral test [48], results suggested that the third factor represented the disengagement, and self-blame. “break point” of the graph, warranting the retention of a 3-factor solution. Results of the parallel analysis [50] suggested that a Data Analysis 2-factor solution was best. Participants were divided into two samples that did not Given the discrepancy between the parallel analysis and other meaningfully differ on PVGP-related variables or demographic factor retention methods, the CFI and RMSEA fit indices were characteristics (henceforth referred to as the validation and examined as well. Results of the 2-factor EFA yielded a CFI of 2 cross-validation samples). Exploratory factor analyses using .94 and an RMSEA of .06, χ (34) = 99.98, p < .001; whereas, the 2 geomin rotation were then conducted on the validation sample, 3-factor solution generated a CFI of .98 and an RMSEA of .04, χ and these models were subsequently tested via confirmatory (25) = 44.75, p = .01. A chi-square model comparison suggested factor analyses with the cross-validation sample. Separate that these models were statistically different from one another, 2 analyses were conducted for the PVGP-R, the VGU, and the VGAS, ∆ χ (9) = 55.23, p < .01. However, the fit indices suggest that respectively. Factor inclusion was determined using the following both models had good fit. The specific loadings for each item on the resulting factors were statistically evaluated to determine techniques: the screen test [48], which involves an examination how items grouped across factors for the two models. To make of the scree plot, the Kaiser-Guttman rule, in which factors with this determination, the standard errors of the factor loadings eigenvalues greater than 1 are included in the model [49], and were used to examine fit by determining significance using the parallel analysis, for which each factor’s eigenvalue is compared z-statistic. The two-tailed Bonferroni critical value was calculated with those obtained from a random data matrix [50]. Further, at α = .05 by accounting for the number of factors and items internal consistency was examined using Cronbach’s alpha. Factor to adjust for alpha inflation, yielding a critical z-statistic of 3.02 © analyses were conducted using Mplus 5.21 [51]. Once sufficient for the 2-factor and 3.10 for the 3-factor solutions. Thus, all results had been achieved with the EFA, a confirmatory factor estimated/standard error values for the item loadings that analysis (CFA) was conducted on the cross-validation sample. The exceeded the respective number loaded significantly on that fit of the CFA model to the cross-validation data was determined factor, regardless of the loading value itself. Results of the 2-factor using the root mean square error of approximation (RMSEA) [52, solution suggested that 10 items of the VGU significantly loaded 53] and the comparative fit index (CFI; Bentler, 1990) [54]. on only one factor and one item loaded on two factors; whereas, for the 3-factor solution, two items cross-loaded. Overall, the As we also aimed to compare the relationships between various 3-factor solution generated good fit indices, appeared to have a measures of PVGP and constructs theoretically related to better fit based on a chi-square model comparison, and seemed addiction, correlation matrices were calculated for each measure most appropriate based on the Kaiser-Guttman rule as well as of PVGP (PVGP-R, VGU, and our extracted VGAS score) and the BIS- 11, PHQ-9, B-YAACQ, and family history of addiction. Correlation visual examination of the scree plot. Thus, the 3-factor solution, matrices were then compared using Fisher’s transformation which accounted for 54.99% of the variance, was adopted. [55], which converted r values into z-scores. Thus, the strength To determine if this model accurately reflected the data, a CFA of of these relationships was statistically compared to determine the 3-factor model was conducted on the cross-validation sample. if one scoring method yielded significantly stronger correlations Results suggested mediocre fit using both CFI (.91) and RMSEA with variables generally associated with addictive disorders. (.07), χ2 (40) = 128.39, p < .001. The VGU’s internal consistency was Lastly, SEM was used to develop a model that accurately described calculated across the entire sample (validation and cross-validation the relationship between variables. Modeling was conducted combined), yielding a Cronbach’s alpha of .77, which is similar to the using Mplus© 5.21 [51]. Prior to data collection, it was difficult value provided by the measure’s author (α = .78; 36) to develop hypotheses about how the specific variables would PVGP-R: An initial CFA was conducted on the PVGP-R items interact, although model fit was examined to determine the best within the validation sample using the 6-factor solution provided 6 This article is available from: www.psychopathology.imedpub.com Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

by Tolchinsky [35]. This yielded inadequate fit, as measured by As with the previous models, a CFA of the 3-factor solution was the CFI (.88) and RMSEA (.07), χ2 (309) = 1124.35, p < .001. Thus, conducted on the cross-validation sample (Figure 1). Results an EFA was conducted on the 27 items of the PVGP-R and the suggested good model fit, based on the CFI (.96) and RMSEA (.06), aforementioned factor retention methods analyzed. Specifically, χ2 (30) = 83.43, p < .001. This suggests that the model generated the Kaiser-Guttman rule [49] advocated that a 6-factor solution as part of the EFA was accurately representing the data in CFA was best, the scree plot (Cattel, 1966) [48] was suggestive of when loadings were constrained to the a priori relationship. a 4-factor solution, and the parallel analysis [50] indicated a The internal consistency of the aggregated model was α = .81. 4-factor solution kept eigenvalues above chance values. The Examination of the item loadings within the model suggests that fit indices of both the 4-factor and 6-factor solutions were the first factor captures tolerance and withdrawal as well as the compared: the 4-factor solution resulted in poor fit (CFI = .88, associated lack of control when trying to stop. The second factor RMSEA = .08, χ2 (249) = 1029.81, p < .001) and the 6-factor highlights consequences related to the amount of time spent solution generated better fit (CFI = .93, RMSEA = .07, χ2 (226) = playing as well as physical pain. Lastly, the third factor addresses 702.56, p < .001). A chi-square model comparison suggested that both social problems and failure to fulfill role obligations. the 6-factor solution may better represent the data, ∆ χ2 (23) = To evaluate the criterion validity of the three measures of PVGP, 327.25, p < .001, and was chosen for further examination. The each score was correlated with the BIS-11, PHQ-9, B-YAACQ, AUDIT, resulting 6-factor solution accounted for 64.93% of the variance. COPE, SCS, and family history of addiction. Examination of the Loadings were evaluated using an adjusted two-tailed Bonferroni correlation coefficients (Table 3) yielded expected relationships critical value converted into a critical z-statistic of 3.55. All items with related variables, including inattention, particular methods significantly loaded on at least one factor, though five items of coping, self-consciousness, substance use, and acquaintances/ significantly loaded on more than one factor. peers suffering from videogame-related issues. To determine if this model accurately reflected the data, a CFA To compare the strengths of the relationships between the three of the 6-factor model was conducted on the cross-validation measures of PVGP and associated criterion variables, Fisher’s sample. Results suggested poor fit using CFI (.88) and mediocre r-to-z transformation analyses were computed [55]. Two z-values fit using RMSEA (.07), χ2 (304) = 1074.41, p < .001. The internal can then be statistically compared in order to obtain a p-value consistency of the PVGP-R was calculated across the entire and determine if they significantly differ from each other. Further, sample (validation and cross-validation combined), yielding a the positive or negative value of the z-score can help determine Cronbach’s alpha of .92. the direction of the correlation, ensuring that relationships are VGAS: Because the aggregated scale was created for the manifested in expected ways. Thus, three calculations were purposes of the current study and no a priori model existed, an computed for each criterion variable: a comparison of PVGP-R EFA was conducted on the 10 items used for the VGAS. While to VGU, PVGP-R to the VGAS, and VGU to the VGAS. The Z-critical the Kaiser-Guttman rule [49] suggested that two factors would values for p < .05 and p < .01 are 1.96 and 2.58 respectively (Table 4). be appropriate, the third factor generated an eigenvalue of 0.99, Taken together, results suggest that the VGAS exhibits good which is just below the cutoff. Further, the screen test [48] and criterion validity when compared to measures of impulsivity, parallel analysis [50] corroborated the 2-factor solution as most depression, less adaptive coping strategies (e.g., denial, substance appropriate. Interestingly, when examining the fit indices for use, behavioral disengagement, self-blame), self-consciousness the 2-factor (CFI = .96, RMSEA = .07, χ2 (26) = 80.29, p < .001) and social anxiety when in public, and alcohol use. Results also and 3-factor (CFI = .99, RMSEA = .03, χ2 (18) = 26.36, p = .09) indicate that the factor analytic structure of the VGAS is excellent solutions, the chi-square test of model fit yielded non-significant (CFI = .99, RMSEA = .03, χ2(18) = 26.36, p = .09) and accurately results for the 3-factor solution. Non-significance denotes that represents the data from a separate sample (CFI = .96, RMSEA = the sample covariance matrix does not differ from the population .06, χ2(30) = 83.43, p < .001). Lastly, the VGAS has good internal covariance matrix (null hypothesis), which is the desired result. consistency, as measured by Cronbach’s Alpha (α = .81). While chi-square is typically sensitive to sample size and can generate significant results even in good models that utilize a The structural characteristics that were deemed to contribute to large number of participants [56, 57], the aforementioned non- longer playing times were also analyzed (Table 5). Specifically, 11 significance speaks to the 3-factor solution’s strength. A chi- items were evaluated via 3x2 ANOVAs, which were all significant square model comparison was conducted to compare the two and ranged from F(5,902) = 7.39, p < .001 (different story solutions; results indicated that they statistically differed from outcomes) to F(5,902) = 14.71, p < .001 (emotional investment one another, ∆ χ2 (8) = 53.93, p < .001. Given that the 3-factor in a character). Further, all genre main effects were significant as solution generated robust fit indices and accounted for 61% of the well in the same pattern (i.e., MMORPG > Shooter > Other). Only variance, it was selected for subsequent analyses. Examination of three structural characteristics yielded significance, including the item loadings within the model suggests that the first factor ability to correct mistakes by reloading a save file, F(1,902) = captures underlying mechanisms of tolerance and withdrawal as 4.04, p = .05, emotional investment in a character, F(1,902) = well as the associated loss of control over use. The second factor 9.57, p = .002, and sound, F(1,902) = 9.00, p = .003. Post-hoc highlights issues related to the amount of time spent playing analyses confirmed that greater endorsement of these items as well as physical pain (i.e., likely a function of excessive play was observed for the high VGAS group. There were no significant time). Lastly, the third factor addresses both social problems and interactions, including no interaction between game genre a failure to fulfill social role obligations (Table 2). and structural characteristics. While each variable was tested © Under License of Creative Commons Attribution 3.0 License 7 Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

separately within the models highlighted below, the summed generated from adapting the substance use disorder (SUD) criteria characteristics associated with longer playtime fit notably better of the DSM-5 [12], i.e., our VGAS, had the best overall model fit. each time; thus, descriptions of the following models all refer to Further, when models were tested via confirmatory methods, the total of the structural characteristics rated as most related only the VGAS yielded a good fit. Thus, the addiction model of to playing longer. Specifically, these were the ability to correct problematic videogame playing seems to represent the current mistakes by reloading a save file, emotional investment in a game data well. Examination of the VGAS’ factor structure highlighted character, and sound. that the first factor captured items assessing unsuccessful efforts to play less (criterion 2), spending more time/money for the same To further evaluate whether “problematic play” might best be excitement (criterion 10), and becoming restless/irritable when conceptualized as addictive behavior, we tested models that cutting down (criterion 11). Thus, this factor appears to capture included variables that are generally associated with addiction the underlying mechanisms of tolerance and withdrawal as well to predict VGAS scores. The variables described above were as the associated lack of control when trying to stop. The second evaluated via structural equation modeling using Mplus© 5.21 factor encapsulated playing for longer than intended (criterion [51]. A number of models were tested, with the best fit obtained 1), spending increasing amounts of time playing (criterion 3), by the final model, which hypothesized that VGAS scores would spending less time with family/friends (criterion 7), experiencing be predicted by the measures of playtime, impulsivity, and physical pain (criterion 9), and planning future games when not coping strategies. Further, weekly playtime and the structural playing (criterion 4). Thus, the second factor consisted of the characteristics were allowed to correlate. Lastly, playtime was items addressing time playing and not doing other activities assumed to be predicted by impulsivity. This model is visually as well as playing games to the point of experiencing physical represented in Figure 2. Results yielded good model fit based on pain. Collectively, these items seem to capture the narrow the CFI (.97) and nearly good fit using the RMSEA (.07), χ2 (2) = reinforcement menu that is common to other forms of addiction, 11.79, p = .003. This suggests that impulsivity likely leads to longer wherein the primary source of reward and motivation comes from playtime, which in turn relates to the videogame characteristics substance/activity to which one is “addicted,” to the exclusion that players’ value. Lastly, maladaptive coping strategies, weekly of other available reinforcers. Lastly, the third factor included playtime, and valued structural characteristics all predict VGAS preoccupation with how to play future games, as well as skipping scores. class/work to play (criterion 5) and getting into arguments with Discussion others (criterion 6). Thus, the last factor captured primarily psychosocial problems associated with game play. Interestingly, The current study sought to explore the concept of problematic it was the item addressing craving/preoccupation that cross- videogame playing (PVGP), comparing the most notable PVGP loaded; suggesting that elements of having a strong desire to play measures to each other in order to determine the best instrument videogames when not actually playing may relate to time spent for capturing the phenomenon of excessive game playing. The playing as well as psychosocial difficulties. The VGAS is available large sample recruited for the current study allowed for a variety in Appendix A. of statistical analyses to be conducted, including modeling on subsamples that require hundreds of individuals. Thus, this While this factor structure appears to fit the current data, it paper represents one of the first systematic attempts to directly does not match the factor analytic investigations of substance compare measures of PVGP on their factor analytic structure, and use disorders. Although less research is available on the current it is the first to explicitly address the extent to which PVGP fits criteria for SUD within DSM-5 [12], several studies have combined with an addiction model akin to that applied to substance use the abuse and dependence criteria from DSM-IV-TR [58], which disorders. collectively align with the current conceptualization of SUD. Thus, when combining the DSM-IV abuse and dependence Results of exploratory factor analyses suggested that the scale criteria together, Saha et al. [59] yielded 1-factor solutions for Table 2 Exploratory Factor Structure of the Videogame Addiction Scale (VGAS). Factor 1 Factor 2 Factor 3 Tolerance/ Amount of Social Problems/ Failure to Fulfill Role Withdrawal/ Time/Craving Obligations Loss of Control 1) Play for longer period than intended .01 .73 -.20 2) Unsuccessfully tried to play for less time .79 -.01 -.26 3) Spend increasing amount of time playing -.04 .85 -.01 4) Planning how I will play my next game -.01 .57 .31 5) Skipped class/work to play .21 .10 .44 6) In order to play, I get into arguments with people .26 -.01 .62 7) Spent less time with family/friends .16 .59 .01 8) Experienced neck, wrist, hand, or back pain or headaches .14 .42 .17 9) Spend more time/money to feel same excitement .39 .10 .12 10) Become restless/irritable when cutting down .83 -.01 .01 Bold loadings represent p < .05 8 This article is available from: www.psychopathology.imedpub.com Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

Unsuccessful in playing less ε

Factor I Spend more timemoney for same excitement ε

ecome restlessirritale when cutting down ε

Play for longer than intended εε

Spend increasing amount of time playing εε Factor II Spend less time with familyfriends εε

Experience physical pain or headaches εε

Planning how I will play next game εε

Sipped classwor to play ε Factor III

In order to play, get into arguments ε

Figure 1 CFA model of the EFA factor structure.

Maladaptive oping -.06

.35 eely Playtime .29 Impulsivity Videogame Addiction Scale (estimated

.11 .30 Structural haracteristics

Figure 2 Structural equation model of videogame playing addiction and associated variables.

amphetamines, cocaine, prescription drugs, tranquilizers, and association between variables followed expected patterns, based opioids (CFIs = .97 to .99, RMSEAs = .03 to .05). Similar results were on the SUD literature. Specifically, the following variables were found by Fulkerson, Harrison, and Beebe [60] across two samples included in the model: impulsivity, maladaptive coping, weekly (Goodness-of-Fit = .99, RMSEA = .05 for both). Additionally, playtime, and the structural characteristics associated with unidimensional models were presented by Rounsaville, Bryant, longer playtime. A brief overview of the literature with regard to Babor, Kranzler, and Kadden [61] and Nelson, Rehm, Ustun, analogous substance-related variables is presented below. Grant, and Chatterji [62]. Taken together, this suggests that Elevated impulsivity, as measured by self-report, has been while videogame addiction may be best assessed using the same observed among those who experience SUDs involving criteria as for SUDs, the relationships between these criteria psychostimulants [63], opiates [64-66], alcohol [67], and ecstasy differ. That is, our results did not support the unidimensional [68, 69]. Additionally, elevated impulsivity scores have been model proposed in the SUD literature. observed among those with pathological gambling [70, 71]. Taken together, these results suggest that impulsivity should be Model of Videogame Addiction included in an adapted model of videogame addiction. Utilizing the results obtained from the previous analyses, a model When examining the literature regarding coping mechanisms, of videogame addiction was constructed to help determine if the © Under License of Creative Commons Attribution 3.0 License 9 Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

Table 3 Correlation Coefficients for Measures of PVGP and Criterion Variables. Videogame PVGP-R VGU Addiction Scale Total Score for the PVGP-R Total Score for the VGU .61** Total Score for the Videogame Addiction Scale .86** .74** Cognitive/Attentional Subscale of the BIS-11 Scale .28** .29** .30** Motor Subscale of the BIS-11 .22** .25** .26** Non-planning Subscale of the BIS-11 .07 .20** .16** Total Score for PHQ-9 .37** .36** .37** Active Coping Subscale of the COPE .02 -.11** -.08* Planning Subscale of the COPE .02 -.07* -.04 Positive Reframing Subscale of the COPE .07* -.03 .01 Acceptance Subscale of the COPE .09** -.02 .01 Humor Subscale of the COPE .20** .14** .16** Religion Subscale of the COPE -.03 -.05 .02 Using Emotional Support Subscale of the COPE .02 -.04 -.04 Using Instrumental Support Subscale of the COPE .04 -.02 -.01 Self-Distraction Subscale of the COPE .23** .15** .13** Denial Subscale of the COPE .23** .19** .27** Venting Subscale of the COPE .19** .14** .18** Substance Use Subscale of the COPE .19** .18** .21** Behavioral Disengagement Subscale of the COPE .29** .24** .32** Self-Blame Subscale of the COPE .23** .20** .21** Private Self-Consciousness Subscale of the SCS .15** .03 .06 Public Self-Consciousness Subscale of the SCS .17** .04 .10** Social Anxiety Subscale of the SCS .18** .10** .14** Total Score for the B-YAACQ .14** .17** .18** Total score for AUDIT Items .15** .15** .14** Total # of People in Life that have Gambling Problem .05 .07* .06 Total # of People in Life that have AOD Problem .07* .05 .06 Total # of People in Life that have Videogame Problem .16** .14** .15** Note: * p < .05; ** p < .01 different coping strategies are generally classified into one of two play [80], it was deemed appropriate to conceptually separate categories: active coping, which often involve problem-solving, playtime from scores of videogame addiction. planning, and help-seeking behaviors; and avoidant/negative Lastly, in line with literature that “route of administration” within coping, which includes denial, self-distraction, behavioral SUD forms of addiction can act as a determinant of problematic use, disengagement, and substance use [72]. Negative coping strategies the structural characteristics were included in the overall model. have been associated with or predicted SUDs in a variety of That is, structural characteristics were regarded as mechanisms populations, including homeless adults [73], incarcerated individuals for more rapid exposure to potentially addictive elements of [74], manufacturing workers [75], working professionals [76], gaming. Therefore, given that these structural characteristics adolescents [77, 78], abuse victims [79], and so on. Thus, it appears essentially address the user experience and interaction with that across a variety of demographic characteristics, engaging in a game, the literature regarding route of substance delivery maladaptive coping behaviors may lead to or at least be associated was examined. For example, Hatsukami and Fischman [81] with SUDs. Interestingly, the coping strategies most related to highlight that the difference in addiction levels between cocaine VGAS scores in the current study were the following: behavioral hydrochloride (snorted) and crack cocaine (smoked) is primarily disengagement, denial, self-blame, and substance use. All of these due to the rate of onset as well as the intensity and duration of constitute avoidant/negative coping strategies, making the current the effect. Further, many users of crack cocaine actually began findings commensurate with the broader SUD literature. with intranasally-administered cocaine hydrochloride and shifted delivery mechanisms to smoking crack [82-85]. Thus, while the For the purposes of evaluating an addictions model within the two types of cocaine are chemically the same, the characteristics current study, weekly playtime was conceptualized as a measure of route of administration play a role. Similarly, games that allow of “exposure.” This made conceptual sense, given that playtime players to correct mistakes by reloading a saved file, feel an is a direct measure of the amount of hours one is exposed to emotional connection to a character, and experience immersive the desired stimulus; in this case videogames. Further, given that sound may also exhibit a higher addictive potential as a function playtime is not necessarily indicative of problematic videogame of how they “deliver” the game.

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Table 4 Z-scores for Comparisons of the Three PVGP Measures. PVGP-R & Addiction PVGP-R & VGU VGU & Addiction Scale Scale Cognitive/Attentional Subscale of the BIS-11 Scale -0.23 -0.37 -0.14 Motor Subscale of the BIS-11 -0.78 -1.03 -0.25 Non-planning Subscale of the BIS-11 -3.00** -1.95 1.05 Total Score for PHQ-9 0.24 0.02 -0.22 Active Coping Subscale of the COPE 2.77** 2.10* -0.66 Planning Subscale of the COPE 1.88 1.32 -0.56 Positive Reframing Subscale of the COPE 2.08* 1.49 -0.59 Acceptance Subscale of the COPE 2.44* 1.71 -0.73 Humor Subscale of the COPE 1.29 0.97 -0.33 Religion Subscale of the COPE 0.38 -1.17 -1.55 Using Emotional Support Subscale of the COPE 1.34 1.28 -0.06 Using Instrumental Support Subscale of the COPE 1.30 1.06 -0.23 Self-Distraction Subscale of the COPE 1.81 2.07* 0.26 Denial Subscale of the COPE 0.90 -0.90 -1.80 Venting Subscale of the COPE 1.02 0.29 -0.74 Substance Use Subscale of the COPE 0.20 -0.46 -0.66 Behavioral Disengagement Subscale of the COPE 1.18 -0.84 -2.03* Self-Blame Subscale of the COPE 0.67 0.38 -0.29 Private Self-Consciousness Subscale of the SCS 2.45* 1.95 -0.50 Public Self-Consciousness Subscale of the SCS 2.68** 1.59 -1.09 Social Anxiety Subscale of the SCS 1.66 0.82 -0.83 Total Score for the B-YAACQ -0.73 -0.97 -0.23 Total score for AUDIT Items -0.04 0.28 0.32 Total # of People in Life that have Gambling Problem -0.26 -0.13 0.13 Total # of People in Life that have AOD Problem 0.50 0.26 -0.24 Total # of People in Life that have Videogame Problem 0.56 0.22 -0.33 Note: * p < .05; ** p < .01

Table 5 F-values for Structural Characteristic x VGAS ANOVA regarding Longer Playtimes. Genre Full Model Characteristic Main Effect Interaction Main Effect (5,902) (1,902) (2,902) (2,902) 3) Correct Mistakes 9.37** 11.51** 4.04* 1.65 4) Emotional Investment 14.71** 13.71** 9.57** 1.43 5) Complex Game Story 9.55** 9.58** 2.73 0.63 6) Diff. Story Outcomes 7.39** 9.54** 2.28 0.18 7) Leveling Up 8.26** 9.98** 0.07 2.35 8) Earning XP 8.43** 8.98** 0.58 2.44 9) Rare Items 8.58** 10.20** 0.05 2.42 11) Meta-Game Rewards 7.65** 8.23** 0.50 1.09 13) Fast Loading 6.28** 10.73** 3.39 0.06 14) Visual Aspects 11.62** 9.36** 1.10 2.23 15) Sound 12.68** 10.88** 9.00** 1.54 Note: *p < .05, **p < .01 The overall best-fitting model highlighted that impulsivity predicts variables is commensurate with what would be expected based weekly playtime; thus, individuals with higher self-reported on the previously discussed addiction literature. Thus, similar impulsivity tend to play more throughout the week. Playtime underlying mechanisms may ultimately contribute to the was correlated with the structural characteristics associated manifestation of addiction symptomatology for both videogames with longer playtime, which makes intuitive sense. Specifically, and substances. individuals who report that certain characteristics impact their playtime are thus likely to play longer. Lastly, avoidant/negative Limitations coping, weekly playtime, and the structural characteristics all predicted videogame addiction. The relationships of these The current study has some limitations. First, given that the © Under License of Creative Commons Attribution 3.0 License 11 Acta Psychopathologica 2015 ISSN 2469-6676 Vol. 1 No. 3:16

survey was open to all undergraduate students, it is not possible the participants had to utilize different scales when responding. to calculate response rates. Further, it is unclear if individuals This also created the dilemma of respondents selecting “maybe” who completed the survey represent a specific subsample of without the response being directly equivalent to responding eligible individuals or if results are generalizable to a general “sometimes.” Future studies should examine these responses student population. As noted earlier, the age of videogame using the same metric in order to avoid discrepancies between players may shape the reported reasons for playing [86]; even if scales. all individuals are playing the same game. Thus, our results may only capture the characteristics of videogame addiction as well Conclusion as the relationship of associated variables within a Midwestern To date, many measures of problematic videogame play have college population. However, given the ubiquity of videogame been utilized in the literature without any systematic investigation play among college students as well as the possibility for addiction of which is the most appropriate/accurate or if common variables to negatively impact students’ academic/career trajectories, this exist across measures. This lack of a standardized definition was is still a population worth investigating. cited as one of the major reasons for classifying Internet Gaming Additionally, except for the PVGP items, it was permissible for Disorder (IGD) as a “condition for further study” within DSM-5 participants to skip survey items, and, as such, there was some and not as a diagnosable disorder [19]. Further, while the term missing data throughout the database. Thus, it is unclear if “addiction” is used loosely across studies, few researchers have there were meaningful differences between participants who sought to adapt the substance use disorder criteria for use in completed a given measure and those who did not. However, with videogame research, which would allow direct comparisons a sample size of over a thousand participants, it was conjectured between drug addiction and behavioral addiction. Thus, this study that a pattern of systematic bias was unlikely, as the sheer number represents the first to compare primary measures of problematic of participants would likely counteract any emerging patterns of videogame playing as well as to combine items across measures bias. to map onto the current criteria for substance use disorder. As The current study also did not allow for teasing apart how different expected, the aggregated questionnaire, which we refer to as the game types influence differences in regard to addiction level or VGAS, was the most compelling measure of videogame-related severity of PVGP. For example, criterion 7 of the VGAS, which impairment, based on both psychometric analysis as well as focuses on the reduction or giving up of social, occupational, criterion validity. These results partially support the criteria of or recreational activities, reflected in item 2 of the PVGP-R Internet Gaming Disorder as outlined in the DSM-5; however, IGD (“Because of my videogame playing, I have spent less time with appears to adapt the Gambling Disorder criteria and vernacular my friends and family”), does not take into account whether the as opposed to utilizing a substance use conceptualization. term “videogame playing” refers to online or offline videogame Thus, additional research is needed to reproduce the current playing. However, the present study did not assess the degree to findings and to lend further support for an addictions model of which participants may have spent less time with some offline problematic videogaming. friends but more time with other online players. Future studies should address this limitation by further investigating the way Although the current study has generated several important different structural characteristics within videogames can affect conclusions, future empirical endeavors can help solidify an the level to which an individual engages in problematic use, addictions-based perspective of problematic videogame play. including assessment of gaming impact on online versus offline Specifically, validation of the VGAS within a clinical sample would social interaction. In addition, future research should examine help ensure that the current conceptualization of addiction is if certain game types are more addictive than others based on reflected in individuals seeking treatment for their impairment. their structural characteristics and how these characteristics can Further, evaluation of a clinical sample would help determine if impact the level of addiction behavior. the VGAS is sensitive to clinical change. It would be anticipated An additional limitation of the current study is that the VGU that scores on the VGAS would drop in response to treatment responses that are measured using a 3-point Likert scale had to or diminishment of symptomatology. Future studies that aim be compared and mapped onto the 5-point scale of the PVGP-R. to identify common biological underpinnings of SUD, other The scales were left intact in their original format in order to addictive behavior, and PVGP will allow for a more encompassing ensure that the questions asked were not redundant and to avoid understanding of the nature of PVGP and the extent to which it corrupting the original measures. Unfortunately, this meant that shares features in common with addictive disorders.

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